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Datasets:
makneeeee
/
sift1b

Tasks:
Feature Extraction
Size:
1B<n<10B
Tags:
vector-search
diskann
nearest-neighbor
benchmark
License:
Dataset card Files
xet
Community
sift1b
File size: 2,256 Bytes
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---
license: other
task_categories:
  - feature-extraction
tags:
  - vector-search
  - diskann
  - nearest-neighbor
  - benchmark
pretty_name: SIFT1B - Sharded DiskANN Indices
size_categories:
  - 1B<n<10B
---

# SIFT1B - Sharded DiskANN Indices

Pre-built DiskANN indices for the SIFT1B (BigANN) dataset, sharded for distributed vector search.

## Dataset Info
- **Source**: [BigANN Benchmarks](http://big-ann-benchmarks.com/)
- **Vectors**: 1,000,000,000 (1 billion)
- **Dimensions**: 128
- **Data type**: uint8
- **Queries**: 10,000
- **Distance**: L2

## DiskANN Parameters
- **R** (graph degree): 64
- **L** (build beam width): 100
- **PQ bytes**: 32

## Shard Configurations
- **shard_2**: 2 shards x 500,000,000 vectors
- **shard_3**: 3 shards x ~333,333,333 vectors
- **shard_5**: 5 shards x 200,000,000 vectors
- **shard_7**: 7 shards x ~142,857,142 vectors
- **shard_10**: 10 shards x 100,000,000 vectors

## File Structure
```
fbin/
  base.u8bin                          # Base vectors (1B x 128 uint8)
  queries.u8bin                       # Query vectors (10K x 128 uint8)
diskann/
  gt_100.bin                          # Ground truth (100-NN)
  shard_N/                            # N-shard configuration
    sift1b_64_100_32.shardX_disk.index                    # DiskANN disk index
    sift1b_64_100_32.shardX_disk.index_512_none.indices   # MinIO graph indices
    sift1b_64_100_32.shardX_disk.index_base_none.vectors  # MinIO vector data
    sift1b_base.shardX.fbin                               # Shard base data
```

### Chunked Files

Files larger than 49 GB are split into chunks for upload:
- `*_512_none.indices.part00`, `.part01`, etc.
- `*_base_none.vectors.part00`, etc. (if applicable)
- `fbin/base.u8bin.part00`, etc.

To reassemble: `cat file.part00 file.part01 ... > file`

## Usage

### Download with huggingface_hub
```python
from huggingface_hub import hf_hub_download

# Download a specific shard file
index = hf_hub_download(
    repo_id="maknee/sift1b",
    filename="diskann/shard_10/sift1b_64_100_32.shard0_disk.index",
    repo_type="dataset"
)
```

### Download with git-lfs
```bash
git lfs install
git clone https://huggingface.co/datasets/maknee/sift1b
```

## License
Same as source dataset (BigANN Benchmarks).